Max Robotics — Global Service Robot Directory + US Certification

About

Chandrayee Basu is a leading researcher in human-robot interaction and machine learning, with a focus on enabling robots to understand and adapt to human preferences. Her work bridges the gap between autonomous systems and human trust, particularly in high-stakes environments like autonomous vehicles. Basu’s major contributions include developing novel frameworks for active learning of reward dynamics, where robots learn from hierarchical queries—such as comparisons and feature-based questions—rather than relying solely on complex demonstrations. This approach makes human guidance more accessible and efficient, as seen in her highly cited 2019 paper (28 citations) on active learning from hierarchical queries. She also advanced trust modeling in human-autonomous vehicle interaction, reviewing how trust dynamics evolve and impact safety (2016, 28 citations). Her 2018 work on learning from richer human guidance (23 citations) further refined how robots can infer objectives through simpler, user-friendly queries. Basu’s research has significant implications for creating more intuitive and trustworthy autonomous systems, earning her recognition for making robot learning more practical and aligned with human expectations.